Statistical Predictive Hybrid Choice Modeling: Exploring Embedded Neural Architecture

Ibrahim A. Nafisah, Irsa Sajjad, Mohammed A. Alshahrani, Osama Abdulaziz Alamri, Mohammed M. A. Almazah and Javid Gani Dar ()
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Ibrahim A. Nafisah: Department of Statistics and Operations Research, College of Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Irsa Sajjad: School of Mathematics and Statistics, Central South University, Changsha 410083, China
Mohammed A. Alshahrani: Department of Mathematics, College of Sciences and Humanities, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
Osama Abdulaziz Alamri: Statistics Department, Faculty of Science, University of Tabuk, Tabuk 47512, Saudi Arabia
Mohammed M. A. Almazah: Department of Mathematics, College of Sciences and Arts (Muhyil), Kind Khalid University, Muhyil 61421, Saudi Arabia
Javid Gani Dar: Department of Applied Sciences, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India

Mathematics, 2024, vol. 12, issue 19, 1-20

Abstract: This study introduces an enhanced version of the discrete choice model combining embedded neural architecture to enhance predictive accuracy while preserving interpretability in choice modeling across temporal dimensions. Unlike the traditional architectures, which directly utilize raw data without intermediary transformations, this study introduces a modified approach incorporating temporal embeddings for improved predictive performance. Leveraging the Phones Accelerometer dataset, the model excels in predictive accuracy, discrimination capability and robustness, outperforming traditional benchmarks. With intricate parameter estimates capturing spatial orientations and user-specific patterns, the model offers enhanced interpretability. Additionally, the model exhibits remarkable computational efficiency, minimizing training time and memory usage while ensuring competitive inference speed. Domain-specific considerations affirm its predictive accuracy across different datasets. Overall, the subject model emerges as a transparent, comprehensible, and powerful tool for deciphering accelerometer data and predicting user activities in real-world applications.

Keywords: machine learning; standard deviation; embedded neural architecture; attention mechanism; smartphone accelerometer; activity recognition; temporal attention; embedded weights (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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